A linear merging methodology for high-resolution precipitation products using spatiotemporal regression

Currently, the only viable option for a global precipitation product is the merger of several precipitation products from different modalities. In this article, we develop a linear merging methodology based on spatiotemporal regression. Four highresolution precipitation products (HRPPs), obtained through methods including the Climate Prediction Center's Morphing (CMORPH), Geostationary Operational Environmental Satellite-Based Auto-Estimator (GOES-AE), GOES-Based Hydro-Estimator (GOES-HE) and Self-Calibrating Multivariate Precipitation Retrieval (SCAMPR) algorithms, are used in this study. The merged data are evaluated against the Arkansas Red Basin River Forecast Center's (ABRFC's) ground-based rainfall product. The evaluation is performed using the Heidke skill score (HSS) for four seasons, from summer 2007 to spring 2008, and for two different rainfall detection thresholds. It is shown that the merged data outperform all the other products in seven out of eight cases. A key innovation of this machine learning method is that only 6% of the validation data are used for the initial training. The sensitivity of the algorithm to location, distribution of training data, selection of input data sets and seasons is also analysed and presented.     

Is Valiant–Vazirani’s isolation probability improvable?

The Isolation Lemma of Valiant and Vazirani (Theor Comput Sci 47:85–93, 1986) provides an efficient procedure for isolating a satisfying assignment of a given satisfiable circuit: Given a Boolean circuit C on n input variables, the procedure outputs a new circuit C′ on the same n input variables such that (i) every satisfying assignment of C′ also satisfies C and (ii) if C is satisfiable, then C′ has exactly one satisfying assignment. In particular, if C is unsatisfiable, then (i) implies that C′ is unsatisfiable. The Valiant–Vazirani procedure is randomized, and when C is satisfiable, it produces a uniquely satisfiable circuit C′ with probability Ω(1/n). Is it possible to have an efficient deterministic witness-isolating procedure? Or, at least, is it possible to improve the success probability of a randomized procedure to a large constant? We prove that there exists a non-uniform randomized polynomial-time witness-isolating procedure with success probability bigger than 2/3 if and only if NP ${\subseteq }$ P/poly. We establish similar results for other variants of witness isolation, such as reductions that remove all but an odd number of satisfying assignments of a satisfiable circuit. We also consider a blackbox setting of witness isolation that generalizes the setting of the Valiant–Vazirani Isolation Lemma and give an upper bound of O(1/n) on the success probability for a natural class of randomized witness-isolating procedures.     

Estimating the motion of plant root cells from in vivo confocal laser scanning microscopy images

Images of cellular structures in growing plant roots acquired using confocal laser scanning microscopy have some unusual properties that make motion estimation challenging. These include multiple motions, non-Gaussian noise and large regions with little spatial structure. In this paper, a method for motion estimation is described that uses a robust multi-frame likelihood model and a technique for estimating uncertainty. An efficient region-based matching approach was used followed by a forward projection method. Over small timescales the dynamics are simple (approximately locally constant) and the change in appearance small. Therefore, a constant local velocity model is used and the MAP estimate of the joint probability over a set of frames is recovered. Occurrences of multiple modes in the posterior are detected, and in the case of a single dominant mode, motion is inferred using Laplace’e method. The method was applied to several Arabidopsis thaliana root growth sequences with varying levels of success. In addition, comparative results are given for three alternative motion estimation approaches, the Kanade–Lucas–Tomasi tracker, Black and Anandan’s robust smoothing method, and Markov random field based methods.     

Scaling and universality in urban economic diversification

Understanding cities is central to addressing major global challenges from climate change to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only recently accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across US metropolitan statistical areas, and provide a framework for measuring the intrinsic diversity of economic activities that transcends scales of the classification scheme. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). We present a simple mathematical derivation of the universality, and provide a model, together with its economic implications of open-ended diversity created by urbanization, for understanding the observed empirical distribution. Given the universal distribution, scaling analyses for individual business categories enable us to determine their relative abundances as a function of city size. These results shed light on the processes of economic differentiation with scale, suggesting a general structure for the growth of national economies as integrated urban systems.     

Managerialism and active citizenship in Britain's reformed health service: power and community in an era of decentralisation

The creation of a large managerial stratum within the British National Health Service in recent years has been one of the most striking characteristics of reforms intended to develop a more efficient and "business-like" service. An accompanying political rhetoric of decentralisation has cast local managerial autonomy as a means to gauge and respond more easily to the needs and preferences expressed by local communities. This article therefore reviews the growth of the new managerial stratum with particular regard to its emerging relationship with the local populations in whose name the organisational reforms have been wrought. The dominant political interpretation of this relationship--that the organisational reforms constitute a movement from leaden "bureaucratic" administration to more locally accountable and responsive managerial regimes--is then tested with regard to an in-depth study of two health authorities responsible for very different local populations. Results show that the role of local populations in influencing decisions and determining priorities is considerably less than inferred by the sustained political rhetoric in favour of the "local voices". Consideration of possible trends in the state and economy suggests however that this disjuncture may not be explicable solely in terms of the new managerial stratum maximising its influence or of central government retaining a high degree of control.     

Respiratory nitric oxide levels in experimental human influenza

The high mobility group HMGI chromosomal proteins are an important component of chromatin. The HMGI-C protein consists of three amino terminal DNA binding domains ("AT hooks"), a linker region and an acidic carboxy domain. In mesenchymal tumors, chromosomal translocations of 12q13-15 result in fusion proteins containing the AT hooks and novel carboxy terminals. We have investigated the status of the HMGI-C gene in two cases of leukemia with anomalies of chromosome 12q and identified three novel isoforms (designated alpha, beta and gamma) derived from alternate splicing. One of the patients expressed all three isoforms, whereas the second patient expressed only the gamma isoform; preferential expression of the HMGI-C gamma isoform was also detected in the leukemic cell lines ML3 and BV173. The results are consistent with a crucial role for truncation of the acidic carboxy domain of HMGI-C in abnormal growth.     

A semantics for a class of non-deterministic and causal production system programs

We define a class of function-free rule-based production system (PS) programs that exhibit non-deterministic and/or causal behavior. We develop a fixpoint semantics and an equivalent declarative semantics for these programs. The criterion to recognize the class of non-deterministic causal (NDC) PS programs is based upon extending and relaxing the concept of stratification, to partition the rules of the program. Unlike strict stratification, this relaxed stratification criterion allows a more flexible partitioning of the rules and admits programs whose execution is non-deterministic or causal or both. The fixpoint semantics is based upon a monotonic fixpoint operator which guarantees that the execution of the program will terminate. Each fixpoint corresponds to a minimal database of answers for the NDC PS program. Since the execution of the program is non-deterministic, several fixpoints may be obtained. To obtain a declarative meaning for the PS program, we associate a normal logic program with each NDC PS program. We use the generalized disjunctive well-founded semantics to provide a meaning to the normal logic program Through these semantics, a well-founded state is associated with and a set of possible extensions, each of which are minimal models for the well-founded state, are obtained. We show that the fixpoint semantics for the NDC PS programs is sound and complete with respect to the declarative semantics for the corresponding normal logic program .This research is partially sponsored by the National Science Foundation under grant IRI-9008208 and by the Institute for Advanced Computer Studies.     

Feature Driven Combination of Animated Vector Field Visualizations

Animated visualizations are one of the methods for finding and understanding complex structures of time-dependent vector fields. Many visualization designs can be used to this end, such as streamlines, vector glyphs, and image-based techniques. While all such designs can depict any vector field, their effectiveness in highlighting particular field aspects has not been fully explored. To fill this gap, we compare three animated vector field visualization techniques, OLIC, IBFV, and particles, for a critical point detection-and-classification task through a user study. Our results show that the effectiveness of the studied techniques depends on the nature of the critical points. We use these results to design a new flow visualization technique that combines all studied techniques in a single view by locally using the most effective technique for the patterns present in the flow data at that location. A second user study shows that our technique is more efficient and less error prone than the three other techniques used individually for the critical point detection task.